Overview

Dataset statistics

Number of variables22
Number of observations2056
Missing cells1730
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory353.5 KiB
Average record size in memory176.1 B

Variable types

Text1
Numeric20
Categorical1

Alerts

BMI is highly overall correlated with HIV/AIDS and 5 other fieldsHigh correlation
HIV/AIDS is highly overall correlated with BMI and 5 other fieldsHigh correlation
thinness 1-19 years is highly overall correlated with BMI and 4 other fieldsHigh correlation
thinness 5-9 years is highly overall correlated with BMI and 4 other fieldsHigh correlation
Adult Mortality is highly overall correlated with HIV/AIDS and 3 other fieldsHigh correlation
Alcohol is highly overall correlated with Income composition of resources and 2 other fieldsHigh correlation
Diphtheria is highly overall correlated with Hepatitis B and 4 other fieldsHigh correlation
GDP is highly overall correlated with Income composition of resources and 5 other fieldsHigh correlation
Hepatitis B is highly overall correlated with Diphtheria and 1 other fieldsHigh correlation
Income composition of resources is highly overall correlated with BMI and 13 other fieldsHigh correlation
Life expectancy is highly overall correlated with BMI and 12 other fieldsHigh correlation
Measles is highly overall correlated with infant deaths and 1 other fieldsHigh correlation
Polio is highly overall correlated with Diphtheria and 4 other fieldsHigh correlation
Schooling is highly overall correlated with BMI and 13 other fieldsHigh correlation
Status is highly overall correlated with Alcohol and 3 other fieldsHigh correlation
infant deaths is highly overall correlated with GDP and 5 other fieldsHigh correlation
percentage expenditure is highly overall correlated with GDPHigh correlation
under-five deaths is highly overall correlated with HIV/AIDS and 6 other fieldsHigh correlation
Alcohol has 136 (6.6%) missing valuesMissing
Hepatitis B has 370 (18.0%) missing valuesMissing
BMI has 24 (1.2%) missing valuesMissing
Total expenditure has 158 (7.7%) missing valuesMissing
GDP has 297 (14.4%) missing valuesMissing
Population has 444 (21.6%) missing valuesMissing
thinness 1-19 years has 24 (1.2%) missing valuesMissing
thinness 5-9 years has 24 (1.2%) missing valuesMissing
Income composition of resources has 110 (5.4%) missing valuesMissing
Schooling has 109 (5.3%) missing valuesMissing
infant deaths has 581 (28.3%) zerosZeros
percentage expenditure has 413 (20.1%) zerosZeros
Measles has 690 (33.6%) zerosZeros
under-five deaths has 536 (26.1%) zerosZeros
Income composition of resources has 83 (4.0%) zerosZeros

Reproduction

Analysis started2024-02-08 05:02:50.295756
Analysis finished2024-02-08 05:04:26.556326
Duration1 minute and 36.26 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct189
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:26.936596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length34
Mean length9.9610895
Min length4

Characters and Unicode

Total characters20480
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st rowMalta
2nd rowCongo
3rd rowBurkina Faso
4th rowGuinea-Bissau
5th rowMyanmar
ValueCountFrequency (%)
of 127
 
4.3%
republic 124
 
4.2%
and 69
 
2.3%
united 48
 
1.6%
the 33
 
1.1%
democratic 32
 
1.1%
guinea 29
 
1.0%
arab 25
 
0.8%
states 25
 
0.8%
saint 24
 
0.8%
Other values (218) 2447
82.0%
2024-02-08T10:34:27.719204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2962
14.5%
i 1796
 
8.8%
e 1474
 
7.2%
n 1473
 
7.2%
r 1134
 
5.5%
o 1105
 
5.4%
927
 
4.5%
t 800
 
3.9%
u 783
 
3.8%
l 761
 
3.7%
Other values (46) 7265
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16666
81.4%
Uppercase Letter 2763
 
13.5%
Space Separator 927
 
4.5%
Open Punctuation 40
 
0.2%
Close Punctuation 40
 
0.2%
Other Punctuation 26
 
0.1%
Dash Punctuation 18
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2962
17.8%
i 1796
10.8%
e 1474
 
8.8%
n 1473
 
8.8%
r 1134
 
6.8%
o 1105
 
6.6%
t 800
 
4.8%
u 783
 
4.7%
l 761
 
4.6%
d 593
 
3.6%
Other values (17) 3785
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 330
 
11.9%
B 225
 
8.1%
C 198
 
7.2%
A 194
 
7.0%
M 186
 
6.7%
R 168
 
6.1%
G 164
 
5.9%
T 141
 
5.1%
P 138
 
5.0%
L 137
 
5.0%
Other values (14) 882
31.9%
Space Separator
ValueCountFrequency (%)
927
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Other Punctuation
ValueCountFrequency (%)
' 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19429
94.9%
Common 1051
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2962
15.2%
i 1796
 
9.2%
e 1474
 
7.6%
n 1473
 
7.6%
r 1134
 
5.8%
o 1105
 
5.7%
t 800
 
4.1%
u 783
 
4.0%
l 761
 
3.9%
d 593
 
3.1%
Other values (41) 6548
33.7%
Common
ValueCountFrequency (%)
927
88.2%
( 40
 
3.8%
) 40
 
3.8%
' 26
 
2.5%
- 18
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20473
> 99.9%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2962
14.5%
i 1796
 
8.8%
e 1474
 
7.2%
n 1473
 
7.2%
r 1134
 
5.5%
o 1105
 
5.4%
927
 
4.5%
t 800
 
3.9%
u 783
 
3.8%
l 761
 
3.7%
Other values (45) 7258
35.5%
None
ValueCountFrequency (%)
ô 7
100.0%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5554
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:28.020403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6179423
Coefficient of variation (CV)0.0023002813
Kurtosis-1.2098211
Mean2007.5554
Median Absolute Deviation (MAD)4
Skewness-0.022799606
Sum4127534
Variance21.325391
MonotonicityNot monotonic
2024-02-08T10:34:28.283986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 137
 
6.7%
2001 136
 
6.6%
2005 133
 
6.5%
2008 132
 
6.4%
2012 132
 
6.4%
2006 131
 
6.4%
2010 130
 
6.3%
2009 129
 
6.3%
2014 129
 
6.3%
2015 129
 
6.3%
Other values (6) 738
35.9%
ValueCountFrequency (%)
2000 128
6.2%
2001 136
6.6%
2002 117
5.7%
2003 123
6.0%
2004 123
6.0%
2005 133
6.5%
2006 131
6.4%
2007 122
5.9%
2008 132
6.4%
2009 129
6.3%
ValueCountFrequency (%)
2015 129
6.3%
2014 129
6.3%
2013 137
6.7%
2012 132
6.4%
2011 125
6.1%
2010 130
6.3%
2009 129
6.3%
2008 132
6.4%
2007 122
5.9%
2006 131
6.4%

Status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Developing
1689 
Developed
367 

Length

Max length10
Median length10
Mean length9.8214981
Min length9

Characters and Unicode

Total characters20193
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloped
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 1689
82.1%
Developed 367
 
17.9%

Length

2024-02-08T10:34:28.567606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T10:34:28.806833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
developing 1689
82.1%
developed 367
 
17.9%

Most occurring characters

ValueCountFrequency (%)
e 4479
22.2%
D 2056
10.2%
v 2056
10.2%
l 2056
10.2%
o 2056
10.2%
p 2056
10.2%
i 1689
 
8.4%
n 1689
 
8.4%
g 1689
 
8.4%
d 367
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18137
89.8%
Uppercase Letter 2056
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4479
24.7%
v 2056
11.3%
l 2056
11.3%
o 2056
11.3%
p 2056
11.3%
i 1689
 
9.3%
n 1689
 
9.3%
g 1689
 
9.3%
d 367
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 2056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20193
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4479
22.2%
D 2056
10.2%
v 2056
10.2%
l 2056
10.2%
o 2056
10.2%
p 2056
10.2%
i 1689
 
8.4%
n 1689
 
8.4%
g 1689
 
8.4%
d 367
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4479
22.2%
D 2056
10.2%
v 2056
10.2%
l 2056
10.2%
o 2056
10.2%
p 2056
10.2%
i 1689
 
8.4%
n 1689
 
8.4%
g 1689
 
8.4%
d 367
 
1.8%

Life expectancy
Real number (ℝ)

HIGH CORRELATION 

Distinct345
Distinct (%)16.8%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.39561
Minimum39
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:29.076162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile51.5
Q163.4
median72.2
Q375.7
95-th percentile82
Maximum89
Range50
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation9.4470945
Coefficient of variation (CV)0.13613389
Kurtosis-0.16667241
Mean69.39561
Median Absolute Deviation (MAD)5.6
Skewness-0.6527533
Sum142261
Variance89.247594
MonotonicityNot monotonic
2024-02-08T10:34:29.409805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 30
 
1.5%
75 23
 
1.1%
78 22
 
1.1%
73.6 20
 
1.0%
74.7 19
 
0.9%
74.9 19
 
0.9%
74.1 19
 
0.9%
74.5 18
 
0.9%
72.8 18
 
0.9%
73.5 18
 
0.9%
Other values (335) 1844
89.7%
ValueCountFrequency (%)
39 1
 
< 0.1%
41 1
 
< 0.1%
41.5 1
 
< 0.1%
42.3 1
 
< 0.1%
43.1 1
 
< 0.1%
44.3 1
 
< 0.1%
44.5 1
 
< 0.1%
44.6 3
0.1%
44.8 2
0.1%
45.1 1
 
< 0.1%
ValueCountFrequency (%)
89 9
0.4%
88 8
0.4%
87 7
0.3%
86 11
0.5%
85 7
0.3%
84 9
0.4%
83.7 1
 
< 0.1%
83.5 1
 
< 0.1%
83.3 1
 
< 0.1%
83 14
0.7%

Adult Mortality
Real number (ℝ)

HIGH CORRELATION 

Distinct390
Distinct (%)19.0%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean163.69659
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:29.725352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3226
95-th percentile396.1
Maximum723
Range722
Interquartile range (IQR)152

Descriptive statistics

Standard deviation123.07552
Coefficient of variation (CV)0.75185149
Kurtosis1.8200378
Mean163.69659
Median Absolute Deviation (MAD)75
Skewness1.1876323
Sum335578
Variance15147.584
MonotonicityNot monotonic
2024-02-08T10:34:30.050174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 21
 
1.0%
11 19
 
0.9%
17 19
 
0.9%
12 18
 
0.9%
144 17
 
0.8%
127 17
 
0.8%
19 17
 
0.8%
14 17
 
0.8%
138 16
 
0.8%
15 15
 
0.7%
Other values (380) 1874
91.1%
ValueCountFrequency (%)
1 11
0.5%
2 2
 
0.1%
3 3
 
0.1%
4 3
 
0.1%
5 2
 
0.1%
6 10
0.5%
7 12
0.6%
8 11
0.5%
9 8
0.4%
11 19
0.9%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
693 1
< 0.1%
686 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%
654 1
< 0.1%
648 1
< 0.1%
647 1
< 0.1%
633 1
< 0.1%

infant deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct175
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.689689
Minimum0
Maximum1800
Zeros581
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:30.374527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q321
95-th percentile88.25
Maximum1800
Range1800
Interquartile range (IQR)21

Descriptive statistics

Standard deviation111.34646
Coefficient of variation (CV)3.8810618
Kurtosis132.03692
Mean28.689689
Median Absolute Deviation (MAD)3
Skewness10.338181
Sum58986
Variance12398.033
MonotonicityNot monotonic
2024-02-08T10:34:30.695146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 581
28.3%
1 255
 
12.4%
2 143
 
7.0%
3 124
 
6.0%
4 65
 
3.2%
8 40
 
1.9%
9 38
 
1.8%
7 37
 
1.8%
10 33
 
1.6%
5 32
 
1.6%
Other values (165) 708
34.4%
ValueCountFrequency (%)
0 581
28.3%
1 255
12.4%
2 143
 
7.0%
3 124
 
6.0%
4 65
 
3.2%
5 32
 
1.6%
6 30
 
1.5%
7 37
 
1.8%
8 40
 
1.9%
9 38
 
1.8%
ValueCountFrequency (%)
1800 1
< 0.1%
1700 2
0.1%
1600 1
< 0.1%
1400 1
< 0.1%
1300 1
< 0.1%
1200 1
< 0.1%
1100 1
< 0.1%
910 1
< 0.1%
576 1
< 0.1%
574 1
< 0.1%

Alcohol
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct933
Distinct (%)48.6%
Missing136
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.5995
Minimum0.01
Maximum17.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:31.005775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8275
median3.745
Q37.7125
95-th percentile12.0005
Maximum17.31
Range17.3
Interquartile range (IQR)6.885

Descriptive statistics

Standard deviation4.065525
Coefficient of variation (CV)0.88390585
Kurtosis-0.79633133
Mean4.5995
Median Absolute Deviation (MAD)3.245
Skewness0.59375287
Sum8831.04
Variance16.528493
MonotonicityNot monotonic
2024-02-08T10:34:31.335301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 212
 
10.3%
0.03 11
 
0.5%
0.09 8
 
0.4%
1.18 8
 
0.4%
0.04 8
 
0.4%
0.05 8
 
0.4%
0.56 7
 
0.3%
0.02 7
 
0.3%
0.21 7
 
0.3%
0.54 7
 
0.3%
Other values (923) 1637
79.6%
(Missing) 136
 
6.6%
ValueCountFrequency (%)
0.01 212
10.3%
0.02 7
 
0.3%
0.03 11
 
0.5%
0.04 8
 
0.4%
0.05 8
 
0.4%
0.06 4
 
0.2%
0.07 3
 
0.1%
0.08 5
 
0.2%
0.09 8
 
0.4%
0.1 6
 
0.3%
ValueCountFrequency (%)
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%
14.97 1
< 0.1%
14.27 1
< 0.1%

percentage expenditure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1644
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean707.31057
Minimum0
Maximum19099.045
Zeros413
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:31.668760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.8529931
median68.236005
Q3432.09439
95-th percentile4257.9811
Maximum19099.045
Range19099.045
Interquartile range (IQR)426.2414

Descriptive statistics

Standard deviation1917.7369
Coefficient of variation (CV)2.7113081
Kurtosis29.25001
Mean707.31057
Median Absolute Deviation (MAD)68.236005
Skewness4.8393914
Sum1454230.5
Variance3677714.7
MonotonicityNot monotonic
2024-02-08T10:34:32.012785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
20.1%
188.1440348 1
 
< 0.1%
660.2777923 1
 
< 0.1%
5579.199083 1
 
< 0.1%
0.388253772 1
 
< 0.1%
325.6807462 1
 
< 0.1%
126.6142137 1
 
< 0.1%
10.21129783 1
 
< 0.1%
42.33443901 1
 
< 0.1%
875.1495189 1
 
< 0.1%
Other values (1634) 1634
79.5%
ValueCountFrequency (%)
0 413
20.1%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
0.661540371 1
 
< 0.1%
ValueCountFrequency (%)
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
15515.75234 1
< 0.1%
14714.82588 1
< 0.1%
12829.25408 1
< 0.1%
12372.05188 1
< 0.1%
11892.33429 1
< 0.1%

Hepatitis B
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)5.0%
Missing370
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean81.137011
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:32.348098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.926234
Coefficient of variation (CV)0.30721163
Kurtosis2.9038566
Mean81.137011
Median Absolute Deviation (MAD)6
Skewness-1.9638013
Sum136797
Variance621.31712
MonotonicityNot monotonic
2024-02-08T10:34:32.671731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 171
 
8.3%
98 135
 
6.6%
97 119
 
5.8%
96 116
 
5.6%
95 108
 
5.3%
94 93
 
4.5%
92 72
 
3.5%
93 67
 
3.3%
89 52
 
2.5%
91 51
 
2.5%
Other values (74) 702
34.1%
(Missing) 370
18.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
0.1%
4 2
 
0.1%
5 6
 
0.3%
6 13
 
0.6%
7 8
 
0.4%
8 31
1.5%
9 50
2.4%
12 1
 
< 0.1%
14 4
 
0.2%
ValueCountFrequency (%)
99 171
8.3%
98 135
6.6%
97 119
5.8%
96 116
5.6%
95 108
5.3%
94 93
4.5%
93 67
 
3.3%
92 72
3.5%
91 51
 
2.5%
89 52
 
2.5%

Measles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct730
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2296.159
Minimum0
Maximum212183
Zeros690
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:32.988224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3383.75
95-th percentile8749.75
Maximum212183
Range212183
Interquartile range (IQR)383.75

Descriptive statistics

Standard deviation11610.468
Coefficient of variation (CV)5.0564738
Kurtosis132.63221
Mean2296.159
Median Absolute Deviation (MAD)17
Skewness10.267962
Sum4720903
Variance1.3480297 × 108
MonotonicityNot monotonic
2024-02-08T10:34:33.300519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 690
33.6%
1 73
 
3.6%
2 50
 
2.4%
3 32
 
1.6%
6 23
 
1.1%
4 22
 
1.1%
8 18
 
0.9%
10 17
 
0.8%
7 17
 
0.8%
5 16
 
0.8%
Other values (720) 1098
53.4%
ValueCountFrequency (%)
0 690
33.6%
1 73
 
3.6%
2 50
 
2.4%
3 32
 
1.6%
4 22
 
1.1%
5 16
 
0.8%
6 23
 
1.1%
7 17
 
0.8%
8 18
 
0.9%
9 15
 
0.7%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
133802 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
110927 1
< 0.1%
99602 1
< 0.1%
90387 1
< 0.1%
88962 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct569
Distinct (%)28.0%
Missing24
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.667421
Minimum1.4
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:33.601873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile5.3
Q119.4
median44.1
Q356.3
95-th percentile65
Maximum87.3
Range85.9
Interquartile range (IQR)36.9

Descriptive statistics

Standard deviation20.084653
Coefficient of variation (CV)0.51942054
Kurtosis-1.2764871
Mean38.667421
Median Absolute Deviation (MAD)16.35
Skewness-0.23493509
Sum78572.2
Variance403.39329
MonotonicityNot monotonic
2024-02-08T10:34:33.923234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.1 12
 
0.6%
59.9 11
 
0.5%
56.6 11
 
0.5%
57 11
 
0.5%
55.8 11
 
0.5%
52.8 11
 
0.5%
57.6 10
 
0.5%
21.3 10
 
0.5%
55.7 10
 
0.5%
17.6 10
 
0.5%
Other values (559) 1925
93.6%
(Missing) 24
 
1.2%
ValueCountFrequency (%)
1.4 2
 
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 7
0.3%
2.2 5
0.2%
2.3 4
0.2%
2.4 4
0.2%
2.5 4
0.2%
2.6 2
 
0.1%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
77.6 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%
76.2 1
< 0.1%
75.7 1
< 0.1%
75.2 2
0.1%
74.8 1
< 0.1%

under-five deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct215
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.991245
Minimum0
Maximum2500
Zeros536
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:34.234349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q326
95-th percentile128.75
Maximum2500
Range2500
Interquartile range (IQR)26

Descriptive statistics

Standard deviation152.77608
Coefficient of variation (CV)3.820238
Kurtosis123.72299
Mean39.991245
Median Absolute Deviation (MAD)4
Skewness9.991267
Sum82222
Variance23340.529
MonotonicityNot monotonic
2024-02-08T10:34:34.555449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 536
26.1%
1 270
 
13.1%
2 112
 
5.4%
4 106
 
5.2%
3 92
 
4.5%
12 39
 
1.9%
6 39
 
1.9%
5 36
 
1.8%
10 35
 
1.7%
8 34
 
1.7%
Other values (205) 757
36.8%
ValueCountFrequency (%)
0 536
26.1%
1 270
13.1%
2 112
 
5.4%
3 92
 
4.5%
4 106
 
5.2%
5 36
 
1.8%
6 39
 
1.9%
7 20
 
1.0%
8 34
 
1.7%
9 25
 
1.2%
ValueCountFrequency (%)
2500 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1600 1
< 0.1%
1500 1
< 0.1%
1100 1
< 0.1%
943 1
< 0.1%

Polio
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)3.5%
Missing11
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean82.794132
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:35.165785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q179
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.348319
Coefficient of variation (CV)0.28200451
Kurtosis3.9048105
Mean82.794132
Median Absolute Deviation (MAD)6
Skewness-2.1313014
Sum169314
Variance545.144
MonotonicityNot monotonic
2024-02-08T10:34:35.482547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 278
 
13.5%
98 169
 
8.2%
97 148
 
7.2%
96 138
 
6.7%
95 131
 
6.4%
94 109
 
5.3%
93 85
 
4.1%
92 76
 
3.7%
91 55
 
2.7%
9 50
 
2.4%
Other values (61) 806
39.2%
ValueCountFrequency (%)
3 4
 
0.2%
4 7
 
0.3%
5 4
 
0.2%
6 8
 
0.4%
7 17
 
0.8%
8 30
1.5%
9 50
2.4%
23 1
 
< 0.1%
24 2
 
0.1%
26 2
 
0.1%
ValueCountFrequency (%)
99 278
13.5%
98 169
8.2%
97 148
7.2%
96 138
6.7%
95 131
6.4%
94 109
 
5.3%
93 85
 
4.1%
92 76
 
3.7%
91 55
 
2.7%
89 44
 
2.1%

Total expenditure
Real number (ℝ)

MISSING 

Distinct740
Distinct (%)39.0%
Missing158
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.9376238
Minimum0.65
Maximum17.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:35.786752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile1.96
Q14.26
median5.73
Q37.53
95-th percentile9.8
Maximum17.2
Range16.55
Interquartile range (IQR)3.27

Descriptive statistics

Standard deviation2.5121307
Coefficient of variation (CV)0.42308687
Kurtosis1.080735
Mean5.9376238
Median Absolute Deviation (MAD)1.61
Skewness0.62429756
Sum11269.61
Variance6.3108004
MonotonicityNot monotonic
2024-02-08T10:34:36.121954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 13
 
0.6%
5.82 9
 
0.4%
4.41 9
 
0.4%
5.6 9
 
0.4%
6.4 8
 
0.4%
8.9 8
 
0.4%
8.2 8
 
0.4%
5.25 8
 
0.4%
4.26 8
 
0.4%
4.2 8
 
0.4%
Other values (730) 1810
88.0%
(Missing) 158
 
7.7%
ValueCountFrequency (%)
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 2
0.1%
1.12 3
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 2
0.1%
1.19 2
0.1%
1.2 2
0.1%
ValueCountFrequency (%)
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.15 1
< 0.1%
15.14 1
< 0.1%
14.55 1
< 0.1%
14.39 1
< 0.1%

Diphtheria
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)3.9%
Missing11
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean82.577017
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:36.551241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q179
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.691192
Coefficient of variation (CV)0.28689813
Kurtosis3.7057486
Mean82.577017
Median Absolute Deviation (MAD)6
Skewness-2.1142122
Sum168870
Variance561.27256
MonotonicityNot monotonic
2024-02-08T10:34:36.916146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 260
 
12.6%
98 161
 
7.8%
95 149
 
7.2%
97 145
 
7.1%
96 137
 
6.7%
94 107
 
5.2%
93 84
 
4.1%
92 80
 
3.9%
91 59
 
2.9%
89 58
 
2.8%
Other values (69) 805
39.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 3
 
0.1%
4 7
 
0.3%
5 5
 
0.2%
6 13
 
0.6%
7 14
 
0.7%
8 34
1.7%
9 43
2.1%
21 1
 
< 0.1%
23 4
 
0.2%
ValueCountFrequency (%)
99 260
12.6%
98 161
7.8%
97 145
7.1%
96 137
6.7%
95 149
7.2%
94 107
5.2%
93 84
 
4.1%
92 80
 
3.9%
91 59
 
2.9%
89 58
 
2.8%

HIV/AIDS
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7509241
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:37.228300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.7
95-th percentile8.825
Maximum50.6
Range50.5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation5.1848318
Coefficient of variation (CV)2.9611973
Kurtosis36.030195
Mean1.7509241
Median Absolute Deviation (MAD)0
Skewness5.4804662
Sum3599.9
Variance26.882481
MonotonicityNot monotonic
2024-02-08T10:34:37.551400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1252
60.9%
0.3 90
 
4.4%
0.2 79
 
3.8%
0.4 48
 
2.3%
0.5 29
 
1.4%
0.6 28
 
1.4%
0.9 24
 
1.2%
0.8 20
 
1.0%
0.7 19
 
0.9%
1.2 15
 
0.7%
Other values (164) 452
 
22.0%
ValueCountFrequency (%)
0.1 1252
60.9%
0.2 79
 
3.8%
0.3 90
 
4.4%
0.4 48
 
2.3%
0.5 29
 
1.4%
0.6 28
 
1.4%
0.7 19
 
0.9%
0.8 20
 
1.0%
0.9 24
 
1.2%
1 9
 
0.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
42.1 1
< 0.1%
40.7 1
< 0.1%
40.2 1
< 0.1%
39.8 1
< 0.1%

GDP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1759
Distinct (%)100.0%
Missing297
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean7286.2002
Minimum1.68135
Maximum115761.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:37.861682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile76.87971
Q1470.01192
median1834.2949
Q35825.0498
95-th percentile41192.866
Maximum115761.58
Range115759.9
Interquartile range (IQR)5355.0379

Descriptive statistics

Standard deviation14006.8
Coefficient of variation (CV)1.9223737
Kurtosis13.21137
Mean7286.2002
Median Absolute Deviation (MAD)1640.5533
Skewness3.3153814
Sum12816426
Variance1.9619044 × 108
MonotonicityNot monotonic
2024-02-08T10:34:38.179109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4178.973369 1
 
< 0.1%
38852.3613 1
 
< 0.1%
2375.1127 1
 
< 0.1%
21.569654 1
 
< 0.1%
6468.471648 1
 
< 0.1%
3831.53819 1
 
< 0.1%
854.346921 1
 
< 0.1%
148.852738 1
 
< 0.1%
2378.33927 1
 
< 0.1%
7813.83499 1
 
< 0.1%
Other values (1749) 1749
85.1%
(Missing) 297
 
14.4%
ValueCountFrequency (%)
1.68135 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
11.147277 1
< 0.1%
11.33678 1
< 0.1%
12.1789279 1
< 0.1%
12.566464 1
< 0.1%
12.989164 1
< 0.1%
13.154199 1
< 0.1%
ValueCountFrequency (%)
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
86852.7119 1
< 0.1%
85948.746 1
< 0.1%
84658.88768 1
< 0.1%
83164.38795 1
< 0.1%
82967.37228 1
< 0.1%

Population
Real number (ℝ)

MISSING 

Distinct1609
Distinct (%)99.8%
Missing444
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean11364203
Minimum34
Maximum1.1796812 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:38.497719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile12239.95
Q1195153.75
median1385316
Q37414499
95-th percentile46431470
Maximum1.1796812 × 109
Range1.1796812 × 109
Interquartile range (IQR)7219345.2

Descriptive statistics

Standard deviation47913217
Coefficient of variation (CV)4.2161529
Kurtosis402.93505
Mean11364203
Median Absolute Deviation (MAD)1358370
Skewness17.654401
Sum1.8319095 × 1010
Variance2.2956763 × 1015
MonotonicityNot monotonic
2024-02-08T10:34:38.876111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1141 2
 
0.1%
444 2
 
0.1%
292 2
 
0.1%
126843 1
 
< 0.1%
1786638 1
 
< 0.1%
15627618 1
 
< 0.1%
19229 1
 
< 0.1%
1547958 1
 
< 0.1%
116985 1
 
< 0.1%
832946 1
 
< 0.1%
Other values (1599) 1599
77.8%
(Missing) 444
 
21.6%
ValueCountFrequency (%)
34 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
367 1
< 0.1%
385 1
< 0.1%
393 1
< 0.1%
ValueCountFrequency (%)
1179681239 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
255131116 1
< 0.1%
248883232 1
< 0.1%
236159276 1
< 0.1%
198686688 1
< 0.1%
196796269 1
< 0.1%
186917361 1
< 0.1%
185546257 1
< 0.1%

thinness 1-19 years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct189
Distinct (%)9.3%
Missing24
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8185039
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:39.193494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.1
95-th percentile13.945
Maximum27.7
Range27.6
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.4165195
Coefficient of variation (CV)0.91657485
Kurtosis3.6337582
Mean4.8185039
Median Absolute Deviation (MAD)2.3
Skewness1.67492
Sum9791.2
Variance19.505645
MonotonicityNot monotonic
2024-02-08T10:34:39.498278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 54
 
2.6%
1.9 47
 
2.3%
0.8 45
 
2.2%
0.7 44
 
2.1%
2.1 44
 
2.1%
1.6 42
 
2.0%
2.2 42
 
2.0%
2 42
 
2.0%
1.2 41
 
2.0%
1.7 40
 
1.9%
Other values (179) 1591
77.4%
ValueCountFrequency (%)
0.1 20
 
1.0%
0.2 30
1.5%
0.3 23
1.1%
0.4 2
 
0.1%
0.5 25
1.2%
0.6 30
1.5%
0.7 44
2.1%
0.8 45
2.2%
0.9 38
1.8%
1 54
2.6%
ValueCountFrequency (%)
27.7 1
< 0.1%
27.4 1
< 0.1%
27.3 1
< 0.1%
27.2 1
< 0.1%
27.1 1
< 0.1%
27 2
0.1%
26.9 1
< 0.1%
26.7 1
< 0.1%
22.2 1
< 0.1%
22 1
< 0.1%

thinness 5-9 years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct195
Distinct (%)9.6%
Missing24
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8705217
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:39.814933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.6
median3.3
Q37.2
95-th percentile13.9
Maximum28.6
Range28.5
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.510071
Coefficient of variation (CV)0.92599342
Kurtosis3.9527244
Mean4.8705217
Median Absolute Deviation (MAD)2.4
Skewness1.7263393
Sum9896.9
Variance20.340741
MonotonicityNot monotonic
2024-02-08T10:34:40.132694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 48
 
2.3%
1.1 48
 
2.3%
0.5 47
 
2.3%
2.1 47
 
2.3%
1.9 43
 
2.1%
1.3 42
 
2.0%
1.7 41
 
2.0%
1 39
 
1.9%
0.6 39
 
1.9%
2 38
 
1.8%
Other values (185) 1600
77.8%
ValueCountFrequency (%)
0.1 26
1.3%
0.2 33
1.6%
0.3 18
 
0.9%
0.4 11
 
0.5%
0.5 47
2.3%
0.6 39
1.9%
0.7 31
1.5%
0.8 26
1.3%
0.9 48
2.3%
1 39
1.9%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28 1
< 0.1%
27.9 1
< 0.1%
27.8 1
< 0.1%
27.7 1
< 0.1%
27.3 1
< 0.1%
22.6 1
< 0.1%

Income composition of resources
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct590
Distinct (%)30.3%
Missing110
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.63125437
Minimum0
Maximum0.948
Zeros83
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:40.454341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29125
Q10.496
median0.682
Q30.779
95-th percentile0.89
Maximum0.948
Range0.948
Interquartile range (IQR)0.283

Descriptive statistics

Standard deviation0.2070298
Coefficient of variation (CV)0.32796572
Kurtosis1.4539806
Mean0.63125437
Median Absolute Deviation (MAD)0.123
Skewness-1.1514877
Sum1228.421
Variance0.042861336
MonotonicityNot monotonic
2024-02-08T10:34:40.773887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83
 
4.0%
0.703 11
 
0.5%
0.734 10
 
0.5%
0.712 10
 
0.5%
0.7 10
 
0.5%
0.723 9
 
0.4%
0.735 9
 
0.4%
0.714 9
 
0.4%
0.686 8
 
0.4%
0.779 8
 
0.4%
Other values (580) 1779
86.5%
(Missing) 110
 
5.4%
ValueCountFrequency (%)
0 83
4.0%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.268 2
 
0.1%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
0.283 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
< 0.1%
0.945 1
< 0.1%
0.939 1
< 0.1%
0.937 1
< 0.1%
0.936 2
0.1%
0.934 1
< 0.1%
0.933 1
< 0.1%
0.932 2
0.1%
0.931 1
< 0.1%
0.93 1
< 0.1%

Schooling
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct164
Distinct (%)8.4%
Missing109
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean12.048998
Minimum0
Maximum20.5
Zeros17
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2024-02-08T10:34:41.082055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.9
Q110.2
median12.4
Q314.3
95-th percentile16.8
Maximum20.5
Range20.5
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.3160271
Coefficient of variation (CV)0.27521185
Kurtosis0.86567984
Mean12.048998
Median Absolute Deviation (MAD)2.1
Skewness-0.57883778
Sum23459.4
Variance10.996036
MonotonicityNot monotonic
2024-02-08T10:34:41.396413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 44
 
2.1%
13.3 40
 
1.9%
12.4 35
 
1.7%
12.5 34
 
1.7%
12.7 34
 
1.7%
12.6 32
 
1.6%
10.7 31
 
1.5%
11.6 31
 
1.5%
11.9 30
 
1.5%
11.7 30
 
1.5%
Other values (154) 1606
78.1%
(Missing) 109
 
5.3%
ValueCountFrequency (%)
0 17
0.8%
2.8 1
 
< 0.1%
2.9 3
 
0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 2
 
0.1%
3.9 1
 
< 0.1%
ValueCountFrequency (%)
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.2%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.5 3
0.1%
19.3 1
 
< 0.1%
19.2 4
0.2%
19.1 3
0.1%
19 3
0.1%

Interactions

2024-02-08T10:34:20.288104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:52.182435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:57.813941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:02.359336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:07.348000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:11.907928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:16.445989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:21.425270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:25.852857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:30.226269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:34.598429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:39.349324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:43.752008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:48.364783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:53.040272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:57.411456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:01.957173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:06.527157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:11.254895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:15.796815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:20.533426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:52.461990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:58.058694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:02.594913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:07.592732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:12.149662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:16.708824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:21.655033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:26.088394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:30.446856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:34.846827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:39.578973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:43.989486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:48.605595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:53.279253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:57.640959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:02.191107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:06.765589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:11.484984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:16.034614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:20.738125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:52.677270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:58.302149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:02.814253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:07.824900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:12.379880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:16.929085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:21.891724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:26.311496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:30.682745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:35.066946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:39.803825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:44.210884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:48.818008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:53.504727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:57.864186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:02.409121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:06.998789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:11.704579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:16.255906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:20.961710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:52.923750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:58.547323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:03.063997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:08.050291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:12.615487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:17.177426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:22.131229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:26.526537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:30.918415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:35.299732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:40.033533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:44.450051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:49.042103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:53.727946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:58.104852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:02.658312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:07.236892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:11.950472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:16.482981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:21.176803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:53.168767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:58.765301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:03.290636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:08.285192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:12.854108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:17.415387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:22.370178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:26.759191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:31.140983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:35.535559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:40.260700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:44.686038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:49.266182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:53.954497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:58.346954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:02.888830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:07.471048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:12.183477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:16.719967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:21.398758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:53.404027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:59.001903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:03.531217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:08.520145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:13.083996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:17.659500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:22.625897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:26.985961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:31.366307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:35.758844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:40.486752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:44.925213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:49.495693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:54.178719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:58.579518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:03.131285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:07.697893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:12.416590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:16.953732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:21.637629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:53.655489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:59.234742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:03.785615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:08.774823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:13.334385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:17.909627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:22.851559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:27.224638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:31.607671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:36.002686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:40.725469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:45.171101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:49.728607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:54.430418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:58.812220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:03.376413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:08.222467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:12.661730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:17.210872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:21.875153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-02-08T10:33:01.693139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:06.644115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:11.248241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:15.779489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:20.745399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:25.220592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:29.565501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:33.965854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:38.698987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:43.111770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:47.660743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:52.124033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:56.779833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:01.274017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:05.858281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:10.595041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:15.094745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:19.637625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:24.203123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:57.293933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:01.914633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:06.868258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:11.486953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:16.018114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:20.984807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:25.434841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:29.804890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:34.183974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:38.922539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:43.328794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:47.896034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:52.636377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:57.001612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:01.516248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:06.086346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:10.812378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:15.325351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:19.862395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:24.417838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:32:57.599534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:02.138429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:07.126417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:11.696796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:16.239208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:21.206392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:25.642918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:30.015769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:34.391650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:39.141451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:43.543946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:48.122387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:52.834960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:33:57.209368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:01.727466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:06.306466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:11.047208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:15.568935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-08T10:34:20.074689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-08T10:34:41.640960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BMIHIV/AIDSthinness 1-19 yearsthinness 5-9 yearsAdult MortalityAlcoholDiphtheriaGDPHepatitis BIncome composition of resourcesLife expectancyMeaslesPolioPopulationSchoolingStatusTotal expenditureYearinfant deathspercentage expenditureunder-five deaths
BMI1.000-0.502-0.574-0.586-0.3930.3440.3190.4790.1820.6100.577-0.2720.308-0.0560.6140.4610.2570.144-0.4750.273-0.488
HIV/AIDS-0.5021.0000.4630.4570.520-0.200-0.439-0.460-0.313-0.637-0.7420.194-0.4510.073-0.6060.123-0.123-0.0480.475-0.2480.501
thinness 1-19 years-0.5740.4631.0000.9470.395-0.477-0.228-0.407-0.053-0.572-0.6070.298-0.2110.074-0.5780.466-0.348-0.0460.456-0.2980.464
thinness 5-9 years-0.5860.4570.9471.0000.421-0.470-0.245-0.415-0.076-0.575-0.6240.322-0.2290.097-0.5830.471-0.367-0.0450.478-0.2990.485
Adult Mortality-0.3930.5200.3950.4211.000-0.223-0.333-0.381-0.233-0.553-0.6530.140-0.3270.097-0.5010.362-0.158-0.0380.391-0.2930.405
Alcohol0.344-0.200-0.477-0.470-0.2231.0000.2910.4280.1260.5250.443-0.1880.272-0.0100.5550.6830.329-0.110-0.3880.296-0.387
Diphtheria0.319-0.439-0.228-0.245-0.3330.2911.0000.3910.8180.5310.531-0.2740.924-0.0900.5290.3050.1500.125-0.4260.224-0.429
GDP0.479-0.460-0.407-0.415-0.3810.4280.3911.0000.2580.6820.625-0.2110.379-0.0310.6550.4900.1320.169-0.5100.796-0.515
Hepatitis B0.182-0.313-0.053-0.076-0.2330.1260.8180.2581.0000.3630.340-0.2260.796-0.1220.3630.1710.0350.101-0.3370.115-0.334
Income composition of resources0.610-0.637-0.572-0.575-0.5530.5250.5310.6820.3631.0000.861-0.2230.521-0.0490.9010.7010.2020.194-0.5860.494-0.597
Life expectancy0.577-0.742-0.607-0.624-0.6530.4430.5310.6250.3400.8611.000-0.2780.516-0.0740.8030.6130.2740.150-0.5940.414-0.612
Measles-0.2720.1940.2980.3220.140-0.188-0.274-0.211-0.226-0.223-0.2781.000-0.2750.293-0.2790.000-0.160-0.0890.570-0.1450.571
Polio0.308-0.451-0.211-0.229-0.3270.2720.9240.3790.7960.5210.516-0.2751.000-0.0970.5190.2970.1210.106-0.4310.204-0.434
Population-0.0560.0730.0740.0970.097-0.010-0.090-0.031-0.122-0.049-0.0740.293-0.0971.000-0.0640.049-0.0640.0270.430-0.0370.422
Schooling0.614-0.606-0.578-0.583-0.5010.5550.5290.6550.3630.9010.803-0.2790.519-0.0641.0000.6310.2710.193-0.6060.480-0.617
Status0.4610.1230.4660.4710.3620.6830.3050.4900.1710.7010.6130.0000.2970.0490.6311.000-0.3050.0070.385-0.2780.383
Total expenditure0.257-0.123-0.348-0.367-0.1580.3290.1500.1320.0350.2020.274-0.1600.121-0.0640.271-0.3051.0000.080-0.1820.152-0.187
Year0.144-0.048-0.046-0.045-0.038-0.1100.1250.1690.1010.1940.150-0.0890.1060.0270.1930.0070.0801.000-0.068-0.056-0.066
infant deaths-0.4750.4750.4560.4780.391-0.388-0.426-0.510-0.337-0.586-0.5940.570-0.4310.430-0.6060.385-0.182-0.0681.000-0.3490.993
percentage expenditure0.273-0.248-0.298-0.299-0.2930.2960.2240.7960.1150.4940.414-0.1450.204-0.0370.480-0.2780.152-0.056-0.3491.000-0.351
under-five deaths-0.4880.5010.4640.4850.405-0.387-0.429-0.515-0.334-0.597-0.6120.571-0.4340.422-0.6170.383-0.187-0.0660.993-0.3511.000

Missing values

2024-02-08T10:34:25.041464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-08T10:34:25.678786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-08T10:34:26.197163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0Malta2008Developed80.064.007.142655.57368486.0166.6072.08.1572.00.121928.76700049379.00.70.70.81314.6
1Congo2005Developing55.3394.082.030.000000NaN14621.71362.02.4262.05.9NaNNaN8.88.50.4969.4
2Burkina Faso2009Developing56.9283.0444.5581.14304792.05411816.17791.07.4192.01.1552.7455521514199.09.38.80.3565.9
3Guinea-Bissau2011Developing57.1289.043.5740.45367486.0023.7785.05.4686.05.7692.6998901596154.07.87.70.4109.0
4Myanmar2007Developing64.5217.0580.260.53057385.0108817.67884.01.6886.00.641.45100049171586.013.213.50.4848.1
5Libya2009Developing72.7132.020.0163.55170398.032959.0298.03.1698.00.11296.973530NaN5.65.40.75714.6
6Canada2014Developing82.065.028.10102.19021755.041866.4291.01.4591.00.1544.43376035544564.00.50.50.91215.9
7Micronesia (Federated States of)2008Developing68.4174.001.970.00000089.0065.2088.012.9485.00.1NaNNaN0.20.20.62811.2
8Bosnia and Herzegovina2010Developing76.494.004.54630.38835689.04553.109.09.5889.00.14611.472980372284.02.62.60.71713.3
9Botswana2009Developing59.2393.025.01426.78556694.018434.7396.06.3996.09.05185.7298451979882.08.48.20.66112.2
CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
2046Lesotho2007Developing46.2633.042.699.1843279.0228.3687.08.4788.030.0918.4327171982287.08.48.30.44010.6
2047Bosnia and Herzegovina2005Developing75.012.004.5645.71354393.0235.5095.08.5093.00.12968.411860378153.02.92.90.00012.5
2048Iraq2011Developing77.0144.0320.17285.11972677.01556.5398.03.3279.00.15854.6144973172753.05.35.10.64910.4
2049Cambodia2013Developing67.8183.0110.018.75821583.0018.21366.05.9383.00.2128.4195681522692.011.011.10.54610.8
2050Saint Lucia2014Developing75.0139.009.970.00000099.0046.7099.06.7299.00.1NaNNaN4.34.30.72313.1
2051Malta2011Developed87.059.006.913601.28745782.0368.0096.09.6096.00.122821.847000416268.00.80.70.82614.8
2052Guinea-Bissau2010Developing56.7287.043.2153.30782783.02623.1782.06.7083.05.9543.957418155588.08.07.90.4058.9
2053Haiti2007Developing61.8266.0176.0856.778587NaN04.72362.05.5663.02.7615.8198199556889.04.24.20.4588.4
2054Italy2003Developed79.972.029.303519.25851595.01098257.0397.08.1796.00.127387.2258005731323.00.50.50.84115.4
2055Eritrea2005Developing59.434.071.075.06468996.01913.9996.02.9796.01.6276.75896039697.09.49.50.0005.4